The invention relates generally to computer implemented supply chain planning and decision support tools, and more particularly to a tool used to manage available to promise resources in a supply chain.
Demand-Supply Rationalization (DSR) software packages are used in manufacturing to ensure that supply is in some sense optimally allocated to prioritized demand. With reference to FIG. 1, these packages can be applied to a supply chain 10, illustrated to comprise three levels or tiers: a Tier 0 or final assembly level, a Tier 1 or subassembly level, and a Tier 2 or component level. It will be understood that the supply chain 10 could have as few as two levels, up to an arbitrarily large number N of levels. Tier 1 includes a demand entity 12, while Tier 1 is illustrated to include first and second supply entities 14 and 16, respectively. The number of Tier 1 supply entities can range from one to an arbitrarily large number M. Demand entity 12 is illustrated to require subassemblies from both the first and second supply entities 14 and 16 to produce a final assembly. Tier 2 is illustrated to include third, fourth, and fifth supply entities 18, 20, and 22, respectively. As with Tier 1, the number of Tier 2 supply entities is arbitrary. First supply entity 14 is illustrated to require components from both the third and fourth supply entities 18 and 20 to produce a first supply entity subassembly. Second supply entity 16 is illustrated to require components from only the fifth supply entity 22 to produce a second supply entity 16 subassembly.
It is known to mathematically or heuristically model a given multi-level and/or multi-supplier per level supply chain 10 using various techniques. For example, with reference to FIG. 2, a centralized supply chain model 30 considers the entire supply chain 10 as a single logical entity, functioning as a vertically integrated supply chain. The centralized supply chain model 30 includes a demand entity database 32 operably coupled to a central model 48. The centralized supply chain model 30 further includes Tier 1 and Tier 2 supply entity databases 36 and 38, respectively, which are operatively coupled to a central database 34 via Tier 1 and Tier 2 supply entity input bridges 40 and 42, respectively. Information contained in the Tier 1 and Tier 2 databases 36, 38 is operated on by Tier 1 and Tier 2 processes 44 and 46, respectively. Communication of information from the supply entity databases 36, 38 to the central database is characterized by a time lag. There are at least three components of the time lag: 1) job scheduling, that is, jobs are setup to run at fixed points in time which vary from enterprise to enterprise thereby creating a gap from the start of the first transmission job at the first enterprise to the start of the last transmission job at the last enterprise; 2) per job transmission time, that is, bulk transfer of large data sets can take minutes to hours to complete; and 3) process evaluation time, that is, a business review process 50 requires time to evaluate step specific results before proceeding to the next step in the end to end process. The central model 48 receives data from the central database 34. Output of the central model 48 is reviewed in the business review process 50 (typically performed by a human user). After a time lag, results of the analysis of the centralized supply chain model 30 (for example, firm orders for available to promise inventory) is communicated to the Tier 1 and Tier 2 supply entities via Tier 1 and Tier 2 supply entity results bridges 52 and 54, respectively.
The central model 48 thus receives all relevant data, from all levels, including all supply entities and the demand entity. With complete information regarding demand needs and supply capability, the central model 48 is capable of performing an end to end analysis over all levels and suppliers, and to develop an optimal allocation of supply to demand using either conventional heuristic or mathematical techniques, such as linear programming, to determine the allocation of components to maximize (that is, optimize) the number of end products ultimately produced by the demand entity 12 based on the components supplied in the demand request.
As a second example of known methods for modeling a supply chain 10, with reference to FIG. 3, it is known to use a so-called “loosely coupled” (or “disconnected”) supply chain model 60. Unlike the centralized supply chain model 30, the loosely coupled supply chain model 60 does not require a central database or supply entity input or results bridges. In the loosely coupled model 60, allocation of supply proceeds from one tier to the next, working from the lowest tier to the top tier. Generally speaking, given a leveli+1 supply support position, and a leveli−1, demand, each leveli entity runs a local allocation of supply to demand. The resultant leveli support position relative to leveli−1 customer demand is then passed back to leveli−1, customers. For example, in the context of the loosely coupled model 60 illustrated in FIG. 3, a demand request is generated by a Tier 0 demand entity process 62. The demand entity process 62 is operatively coupled to a demand entity database 64. The demand request is communicated to Tier 1 supply entity process 66 via a zone 1 dialogue 68 within a collaboration zone 70. That level 0 customer demand is compared to a level 2 (Tier 2) supply support position. That is, a Tier 2 process 72 queries Tier 2 database 74, determines the supply support position, and communicates the supply support position to the Tier 1 process 66 via a zone 2 dialogue 76 within the collaboration zone 70. The supply support position information, combined with information stored in a Tier 1 database 78, allows the Tier 1 process 66 to provide a response to the demand request.
An advantage of the centralized supply chain model 30 is its ability to provide an optimal solution for allocation of available to promise inventory. A central disadvantage of the centralized supply chain model 30 is the relatively high expense (compared to the loosely coupled supply chain model) of providing, maintaining, and using the input and result bridges 40, 42, 52, and 54, and central database 34.
While there are development and operations savings as well as flexibility gains to be realized in use of a loosely coupled model 60, a significant disadvantage with use of the loosely coupled model 60 is that such models are unable to provide a closed, end to end optimal allocation of available to promise inventory. Given current solver techniques, end to end optimum allocation of supply to demand requires inter-level and intra-tier visibility to materials and/or capacity constraints. While this visibility is necessary, it is not sufficient to permit calculation of an optimal allocation of available to promise inventory. Coordinated inter-level and intra-tier allocations are required if the allocation result is to be optimum in terms of the top level demands and business rules. The centralized supply chain model 30 has the necessary visibility and sufficient control to effect end to end optimum allocation of supply to top level demand. The loosely coupled supply chain model 60 does not. Known loosely coupled supply chain models 60 provide suboptimal end to end allocations.
Given a trend away from physical or logical vertically integrated supply chains and single entity models toward dynamic multi-player loosely coupled supply chains, the inability of a loosely coupled model to develop a global optimum allocation of supply to demand is a significant and challenging problem.
Furthermore, there is a second disadvantage to the loosely coupled models 60. Disconnected models 60 typically require days to iteratively develop the equivalent of a closed-form, optimal response to a new top level demand statement. Many, if not most, businesses can ill afford to wait days or perhaps even weeks to respond to a demand change. Hence the time lags inherent in loosely coupled models 60 pose a significant business problem.
A need exists, therefore, for an available to promise inventory allocation tool providing both the time-efficient closed-form optimal allocation solutions characteristic of centralized supply chain models as well as the cost and flexibility benefits of the loosely coupled supply chain models.